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Automatic Camera Calibration by Landmarks on Rigid Objects

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138666" target="_blank" >RIV/00216305:26230/20:PU138666 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.fit.vut.cz/research/publication/12345/" target="_blank" >https://www.fit.vut.cz/research/publication/12345/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00138-020-01125-x" target="_blank" >10.1007/s00138-020-01125-x</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automatic Camera Calibration by Landmarks on Rigid Objects

  • Original language description

    This article presents a new method for automatic calibration of surveillance cameras. We are dealing with traffic surveillance and therefore the camera is calibrated by observing vehicles; however, other rigid objects can be used instead. The proposed method is using keypoints or landmarks automatically detected on the observed objects by a convolutional neural network. By using fine-grained recognition of the vehicles (calibration objects), and by knowing the 3D positions of the landmarks for the (very limited) set of known objects, the extracted keypoints are used for calibration of the camera, resulting in internal (focal length) and external (rotation, translation) parameters and scene scale of the surveillance camera. We collected a dataset in two parking lots and equipped it with a calibration ground truth by measuring multiple distances in the ground plane. This dataset seems to be more accurate than the existing comparable data (GT calibration error reduced from 4.62% to 0.99%). Also, the experiments show that our method overcomes the best existing alternative in terms of accuracy (error reduced from 6.56% to 4.03%) and our solution is also more flexible in terms of viewpoint change and other.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Machine Vision and Applications

  • ISSN

    0932-8092

  • e-ISSN

    1432-1769

  • Volume of the periodical

    32

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    13

  • Pages from-to

    2-15

  • UT code for WoS article

    000575425400001

  • EID of the result in the Scopus database

    2-s2.0-85091965520